# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
DESCRIPTION:
    This sample demonstrates how to run Prompt Agent operations
    using the Code Interpreter Tool and a synchronous client followed by downloading the generated file.

USAGE:
    python sample_agent_code_interpreter.py

    Before running the sample:

    pip install "azure-ai-projects>=2.0.0b1" python-dotenv

    Set these environment variables with your own values:
    1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
       page of your Microsoft Foundry portal.
    2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
       the "Models + endpoints" tab in your Microsoft Foundry project.
"""

import os
import tempfile
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, CodeInterpreterToolAuto

load_dotenv()

endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
    project_client.get_openai_client() as openai_client,
):

    # [START tool_declaration]
    # Load the CSV file to be processed
    asset_file_path = os.path.abspath(
        os.path.join(os.path.dirname(__file__), "../assets/synthetic_500_quarterly_results.csv")
    )

    # Upload the CSV file for the code interpreter
    file = openai_client.files.create(purpose="assistants", file=open(asset_file_path, "rb"))
    tool = CodeInterpreterTool(container=CodeInterpreterToolAuto(file_ids=[file.id]))
    # [END tool_declaration]

    print(f"File uploaded (id: {file.id})")

    # Create agent with code interpreter tool
    agent = project_client.agents.create_version(
        agent_name="MyAgent",
        definition=PromptAgentDefinition(
            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            instructions="You are a helpful assistant.",
            tools=[tool],
        ),
        description="Code interpreter agent for data analysis and visualization.",
    )
    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

    # Create a conversation for the agent interaction
    conversation = openai_client.conversations.create()
    print(f"Created conversation (id: {conversation.id})")

    # Send request to create a chart and generate a file
    response = openai_client.responses.create(
        conversation=conversation.id,
        input="Could you please create bar chart in TRANSPORTATION sector for the operating profit from the uploaded csv file and provide file to me?",
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )
    print(f"Response completed (id: {response.id})")

    # Extract file information from response annotations
    file_id = ""
    filename = ""
    container_id = ""

    # Get the last message which should contain file citations
    last_message = response.output[-1]  # ResponseOutputMessage
    if (
        last_message.type == "message"
        and last_message.content
        and last_message.content[-1].type == "output_text"
        and last_message.content[-1].annotations
    ):
        file_citation = last_message.content[-1].annotations[-1]  # AnnotationContainerFileCitation
        if file_citation.type == "container_file_citation":
            file_id = file_citation.file_id
            filename = file_citation.filename
            container_id = file_citation.container_id
            print(f"Found generated file: {filename} (ID: {file_id})")

    print("\nCleaning up...")
    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
    print("Agent deleted")

    # Download the generated file if available
    if file_id and filename:
        file_content = openai_client.containers.files.content.retrieve(file_id=file_id, container_id=container_id)
        print(f"File ready for download: {filename}")
        file_path = os.path.join(tempfile.gettempdir(), filename)
        with open(file_path, "wb") as f:
            f.write(file_content.read())
        # Print result (should contain "file")
        print(f"==> Result: file, {file_path} downloaded successfully.")
    else:
        print("No file generated in response")
